Lukas Fisch retweeted
This really speeds up preprocessing and shows - yet again - that neural networks are eating software.
Great work, @codingfisch !! Happy to see this finally published.
📢Out now! @codingfisch and colleagues present deepmriprep, a tool that leverages neural networks to enable 37x faster Voxel-based Morphometry preprocessing of MRI data than existing methods. nature.com/articles/s43588-0…
Lukas Fisch retweeted
📢Out now! @codingfisch and colleagues present deepmriprep, a tool that leverages neural networks to enable 37x faster Voxel-based Morphometry preprocessing of MRI data than existing methods. nature.com/articles/s43588-0…
Lukas Fisch retweeted
Our GPU stack for both NVIDIA and AMD, aside from minimal pieces of signed firmware, is 100% open source and pure Python except for the compiler. It's not using vendor drivers, frameworks, or libraries. That's why it's so easy to make it work on Mac.
For compilers, on AMD, we use upstream LLVM, and on NVIDIA, we use the NAK compiler from the MESA project. We plan to replace the compiler with pure tinygrad in a year or two as well.
With RANGEIFY merged, our lowering stuff now matches the state of the art, TVM style. We're studying ThunderKittens and TileLang for speed at that level, and should have all this stuff ready in 200 days for the due date of our AMD Llama 405B training contract.
Due to tinygrad's small size and pure Python nature, it's the easiest ML library to make progress on, aka fastest slope of improvement. With Megakernel style for scheduling, MODeL_opt style for planning, and E-graph style for symbolic, we should blow past the state of the art in PyTorch and JAX speed.
If we do that, NVIDIA's moat is over. It's 1000 lines at most to add a new accelerator to tinygrad. And I don't mean to add a new accelerator with help from a kernel driver, compiler, and libraries. Just 1000 lines of software for the *whole* accelerator speaking right on the PCIe BARs, like what tinygrad is doing with the NVIDIA and AMD GPUs now.
Lukas Fisch retweeted

François Chollet@fchollet7 Sep 2025
United States
United StatesConnected via United States App StoreAccount-level information, not a live location or per-post device.
When you store your knowledge and skills as parametric curves (as all deep learning models do), the only way you can generalize is via interpolation on the curve. The problem is that interpolated points *correlate* with the truth but have no *causal* link to the truth. Hence hallucinations.
The fix is to start leveraging causal symbolic graphs as your representation substrate (e.g. computer programs of the kind we write as software engineers). The human-written software stack, with its extremely high degree of reliability despite its massive complexity, is proof of existence of exact truthiness propagation.
Lukas Fisch retweeted
Couldn't agree more with Linus here. Making your code running as fast as possible (or just faster than someone's else code) is the best feeling you can get from programming
The issue that it is extremely hard to find the project that will pay you for instruction level efficiency
Lukas Fisch retweeted

François Chollet@fchollet13 Jul 2025
United States
United StatesConnected via United States App StoreAccount-level information, not a live location or per-post device.
Our thesis at Ndea: simple theories should be discoverable using little data and little compute.
And everything humans have ever invented is a fairly simple composition of fairly simple theories.
Lukas Fisch retweeted
NCCL sending the loss value from the last pipeline parallel stage back to rank 0 so the user can print it
Lukas Fisch retweeted
I’m speaking at 2pm on Wednesday at the Upper Bound AI conference. I am in the “business of AI” track, but my talk will be about reinforcement learning research.
Video should be available after the conference.
Lukas Fisch retweeted
Replying to @wwu_mmll
@wwu_mmll is looking for a PostDoc (3 years with possible extension). Work will be at the intersection of ML, dynamical systems, and neuroscience. Great team, beautiful city. DM me if you did your PhD on ML/dynamica systems and are interested in working in neuroscience.
Lukas Fisch retweeted
Bernhard Riemann died in 1866 at the age of 39. Here is a list of things named after him.
Riemann bilinear relations
Riemann conditions
Riemann form
Riemann function
Riemann–Hurwitz formula
Riemann matrix
Riemann operator
Riemann singularity theorem
Riemann surface
Compact Riemann surface
The tangential Cauchy–Riemann complex
Zariski–Riemann space
Cauchy–Riemann equations
Riemann integral
Generalized Riemann integral
Riemann multiple integral
Riemann invariant
Riemann mapping theorem
Measurable Riemann mapping theorem
Riemann problem
Riemann solver
Riemann sphere
Riemann–Hilbert correspondence
Riemann–Hilbert problem
Riemann–Lebesgue lemma
Riemann–Liouville integral
Riemann–Roch theorem
Arithmetic Riemann–Roch theorem
Riemann–Roch theorem for smooth manifolds
Grothendieck–Hirzebruch–Riemann–Roch theorem
Hirzebruch–Riemann–Roch theorem
Riemann–Stieltjes integral
Riemann series theorem
Riemann sum
Riemann–von Mangoldt formula
Riemann hypothesis
Generalized Riemann hypothesis
Grand Riemann hypothesis
Riemann hypothesis for curves over finite fields
Riemann theta function
Riemann Xi function
Riemann zeta function
Riemann–Siegel formula
Riemann–Siegel theta function
Free Riemann gas
Riemann invariant
Riemann–Cartan geometry
Riemann–Silberstein vector
Riemann-Lebovitz formulation
Riemann curvature tensor
Riemann tensor
Riemannian graph
Riemannian group
Riemannian holonomy
Riemannian manifold also called Riemannian space
Riemannian metric tensor
Riemannian Penrose inequality
Riemannian polyhedron
Riemannian singular value decomposition
Riemannian submanifold
Riemannian submersion
Riemannian volume form
Riemannian wavefield extrapolation
Sub-Riemannian manifold
Riemannian symmetric space
Riemann's differential equation
Riemann's existence theorem
Riemann's explicit formula
Riemann's minimal surface
Riemann's theorem on removable singularities
Lukas Fisch retweeted
📢 Exciting News! Our recently published paper explores the link between childhood maltreatment, social support, and functional activity during negative emotion processing. Shared first authorship with @AnneRinck_
Lukas Fisch retweeted
🧠🔬JOB Alert - PhD-position 📢
Join our fantastic team @TraPLabMS and let's find out how replicable neuroimaging research in depression really is! Why do some brain findings replicate while others don't? Excited to receive your application by April 10 via tinyurl.com/2dl5qrjp
Lukas Fisch retweeted
Those who would give up essential Simplicity to purchase a little temporary Speed deserve neither Simplicity nor Speed.
Lukas Fisch retweeted
📢Publication Alert!
Check out my new article on long-term effects of electroconvulsive therapy on brain structure in depression published in Psychological Medicine!⚡️🧠
doi.org/10.1017/S00332917230…
🧵
Lukas Fisch retweeted
Had an awesome #TraPLab_Münster lab conference in the Münster University castle




















